Image processing apparatus and method

ABSTRACT

Processing for improving the contrast of an image of interest is executed by an image processing circuit in an X-ray photography apparatus. The image is decomposed into coefficient values of a plurality of frequency bands by a frequency-component decomposition circuit to obtain coefficients of each and every frequency band. A coefficient conversion circuit converts coefficient values with regard to at least one frequency band of the plurality of frequency bands in such a manner that a frequency distribution of coefficient values of every frequency band will become a prescribed frequency distribution. A restoration circuit generates an image using the coefficient values obtained by the conversion performed by the coefficient conversion circuit.

This application is a continuation of application Ser. No. 10/705,269,filed Nov. 10, 2003, the entirety of which is incorporated herein byreference.

FIELD OF THE INVENTION

This invention relates to an image processing apparatus and method. Moreparticularly, the invention relates to an image processing apparatus andmethod for improving the contrast of an image of interest.

BACKGROUND OF THE INVENTION

Owing to recent advances in digital technology, it is now possible toconvert a radiation image or the like to a digital image signal andsubject the digital image signal to image processing such as frequencyprocessing to output the processed image by displaying it on a displaysuch as a CRT or printing it on film by a printer. In such frequencyprocessing, image processing is executed by decomposing the image ofinterest into image coefficients of a plurality of frequency bands andincreasing or decreasing the image coefficient values of every frequencyband individually.

On the other hand, it is common practice to uniformalize a histogram(frequency distribution) of image densities (pixel values) by using adensity histogram uniformalizing method [e.g., see “Digital ImageProcessing [1] for Understanding Images”, pp. 133-135, by JunichiroToriwaki, published by Shokodo, First Edition, Fourth Printing). Usingthis method makes it possible to utilize a given density rangeefficiently so that image contrast is improved.

The above method attempts to obtain a desired frequency processingeffect by changing the values of frequency coefficients. However, thismeans nothing more than altering the strength of coefficient values anddoes not involve the idea of controlling a histogram of coefficientvalues after a coefficient conversion. More specifically, theconventional method of increasing or decreasing coefficient valuesinvolves only increasing or decreasing coefficient values individuallyon a per-frequency-band basis and does not take into consideration acoefficient histogram of each frequency band. This makes it necessary toadjust coefficients by trial and error in order to obtain the desiredeffect. Further, when the value of a frequency coefficient is merelychanged, this has an effect upon the dynamic range of the image after ithas been processed and there are instances where artifacts such asovershoot occur at the edge (contour) of the image.

Further, though image contrast can be improved by uniformalizing apixel-value histogram, as indicated in the reference cited above, thismethod implements gray-level conversion processing and not frequencyprocessing, thereby making it impossible to adjust components on aper-frequency-band basis.

The present inventors have discovered that an image of desired contrastis obtained by controlling the frequency distribution of coefficientvalues of every frequency band in accordance with the particularobjective. In the examples of the prior art mentioned above, however,there is no implementation of a coefficient conversion that takes intoaccount a frequency distribution of coefficient values obtained after acoefficient conversion.

In view of the state of the prior art described above, there is need foran image processing apparatus and method whereby the contrast of animage of interest can be improved efficiently and effectively.

SUMMARY OF THE INVENTION

According to one aspect of the present invention, there is provided, animage processing apparatus comprising:

decomposing means for decomposing an image into components of aplurality of frequency bands;

converting means for converting coefficient values with regard to atleast one frequency band of the plurality of frequency bands in such amanner that a frequency distribution of coefficient values of everyfrequency band will become a prescribed frequency distribution; and

generating means for generating an image using the coefficient valuesobtained by the converting means.

According to another aspect of the present invention, there is provided,an image processing method comprising:

a decomposing step of decomposing an image into components of aplurality of frequency bands;

a converting step of converting coefficient values with regard to atleast one frequency band of the plurality of frequency bands in such amanner that a frequency distribution of coefficient values of everyfrequency band will become a prescribed frequency distribution; and

a generating step of generating an image using the coefficient valuesobtained by the converting step.

Other features and advantages of the present invention will be apparentfrom the following description taken in conjunction with theaccompanying drawings, in which like reference characters designate thesame or similar parts throughout the figures thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate an embodiment of the inventionand, together with the description, serve to explain the principles ofthe invention.

FIG. 1 is a block diagram illustrating the structure of an X-rayphotography apparatus according to an embodiment of the presentinvention;

FIG. 2 is a flowchart illustrating the procedure of image processing inthe X-ray photography apparatus of this embodiment;

FIG. 3 is a diagram illustrating an example of a coefficient conversiontable that uses a histogram uniformalizing method;

FIG. 4 is a diagram illustrating an ideal example of coefficient valuesthat have undergone a coefficient conversion by a coefficient conversiontable that uses a histogram uniformalizing method;

FIG. 5 is a diagram illustrating a conversion table based upon acombination of a coefficient conversion table that uses a histogramuniformalizing method and a coefficient conversion table of linear form;and

FIGS. 6A to 6C are diagrams illustrating a coefficient decompositioncircuit and a restoration circuit that employ a discrete wavelettransform.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

A preferred embodiment of the present invention will now be described indetail in accordance with the accompanying drawings.

Disclosed in this embodiment is an image processing apparatus thatexecutes frequency processing. Specifically, the apparatus convertscoefficient values in such a manner that a frequency distribution ofcoefficient values of every frequency band will take on a desired form.

FIG. 1 is a block diagram illustrating the structure of an X-rayphotography apparatus 100 according to a preferred embodiment of thepresent invention. The X-ray photography apparatus 100 has a functionfor executing effective image processing when an image that has beencaptured by photography is output on film or on a monitor. The X-rayphotography apparatus 100 includes a data acquisition circuit 105, apreprocessing circuit 106, a CPU 108, a main memory 109, a control panel110, an image display unit 111 and an image processing circuit 112.These are interconnected by a CPU bus 107 so as to be capable ofexchanging data with one another.

The data acquisition circuit 105 and preprocessing circuit 106 in theX-ray photography apparatus 100 are interconnected, and atwo-dimensional X-ray sensor 104 and X-ray generating circuit 101 areconnected to the data acquisition circuit 105. Furthermore, the imageprocessing circuit 112 is constructed to include a frequency-componentdecomposition circuit 113, a coefficient conversion circuit 114, arestoration circuit 115 and a changing circuit 116. These circuits areconnected to the CPU bus 107.

The functions of the above-mentioned components of the X-ray photographyapparatus 100 will become clear from the description that follows.

The main memory 109 in the X-ray photography apparatus 100 set forthabove stores a control program and various data necessary for processingby the CPU 108 and also functions as the working memory of the CPU 108.The CPU 108 employs the main memory 109 to control the overall operationof the apparatus in accordance with an operation performed at thecontrol panel 110. As a result, the X-ray photography apparatus 100operates in the manner set forth below.

First, when the user enters a photography command via the control panel110, the command is sent to the data acquisition circuit 105 by the CPU108. Upon receiving the photography command, the data acquisitioncircuit 105 executes X-ray photography by controlling the X-raygenerating circuit 101 and two-dimensional X-ray sensor 104.

In X-ray photography, the X-ray generating circuit 101 emits an X-raybeam 102 toward a specimen 103. The X-ray beam 102 emitted from theX-ray generating circuit 101 passes through the specimen 103 while beingattenuated and arrives at the two-dimensional X-ray sensor 104, whichresponds by outputting an X-ray image signal. In this embodiment, it isassumed that the specimen 103 is a human body. That is, the X-ray imagethat is output from the two-dimensional X-ray sensor 104 is an image ofthe human body.

The data acquisition circuit 105 converts the X-ray image signal, whichis output from the two-dimensional X-ray sensor 104, to a digital signaland supplies the digital signal to the preprocessing circuit 106 asX-ray image data. The preprocessing circuit 106 subjects the signal(X-ray image data) from the data acquisition circuit 105 topreprocessing such as offset correction processing and gain compensationprocessing. The X-ray image data that has undergone preprocessing in thepreprocessing circuit 106 is transferred as original image data to themain memory 109 and image processing circuit 112 via the CPU bus 107under the control of the CPU 108.

The frequency-component decomposition circuit 113 in the imageprocessing circuit 112 subjects the original image to a discrete wavelettransform (DWT) and obtains a coefficient (a wavelet transformcoefficient) of each frequency band. On the basis of a frequencydistribution of the coefficient values of each of the frequency bandsobtained by the frequency-component decomposition circuit 113, thecoefficient conversion circuit 114 generates a coefficient conversiontable that is for the purpose of making this frequency distribution afrequency distribution of a prescribed shape, and uses this coefficientconversion table to convert the frequency coefficients that have beencalculated by the frequency-component decomposition circuit 113.Finally, the restoration circuit 115 subjects the coefficients that havebeen obtained by the coefficient conversion circuit 114 to an inversediscrete wavelet transform (inverse DWT) to obtain an image that hasundergone frequency processing. Further, the changing circuit 116changes the conversion characteristic (conversion table) of thecoefficient conversion circuit 114 based upon a command, etc., from thecontrol panel 110. The image that has been obtained by the restorationcircuit 115 is stored in an external storage device (not shown) or isdisplayed on the image display unit 111.

The operation of the X-ray photography apparatus according to thisembodiment having the structure set forth above will now be described ingreater detail. FIG. 2 is a flowchart illustrating the procedure ofimage processing in the X-ray photography apparatus 100 of thisembodiment.

The original image obtained by the preprocessing circuit 106 istransferred to the image processing circuit 112 via the CPU bus 107 asdescribed above. First, at step S201, the frequency-componentdecomposition circuit 113 of the image processing circuit 112 subjectsthe original image, which is represented by f(x,y), to a two-dimensionaldiscrete wavelet transform, calculates coefficients on aper-frequency-band basis and outputs the coefficients.

The discrete wavelet transform will be described with reference to FIGS.6A and 6B. FIG. 6A is a diagram illustrating the structure of thefrequency-component decomposition circuit 113, and FIG. 6B is a diagramillustrating an example of the structure of a two-level transformcoefficient group obtained by the two-dimensional DWT processing.

The entered image signal is separated into an even-numbered addresssignal and an odd-numbered address signal by a combination of a delayelement and downsamplers, and the address signals are subjected tofilter processing by two filters p and u. In FIG. 6A, referencecharacters s and d represent low-pass and high-pass coefficients,respectively, which prevail when one level of decomposition is appliedto each of the one-dimensional image signals. It is assumed that thesecoefficients are calculated as follows:d _(n) =x _(2n+1)−floor[(x _(2n) +x _(2n+2))/2]s _(n) =x _(2n)+floor[(d _(n−1) +d _(n))/4]where x_(n) represents the pixel value of an original image that is toundergo conversion, and floor [ ] represents a floor function known inthe art.

By virtue of the above processing, a one-dimensional discrete wavelettransform is applied to the image data. A two-dimensional discretewavelet transform is achieved by executing the above-describedone-dimensional transform successively along the horizontal and verticaldirections of the image. As the details of this processing are wellknown, they need not be described here.

FIG. 6B shows an example of the structure of a two-level transformcoefficient group obtained by the two-dimensional DWT processing. Theimage signal is decomposed into coefficient groups HH1, HL1, LH1, . . ., LL of a plurality of different frequency bands. Here HH1, HL1, LH1, .. . , LL are also referred to as subbands and each indicates acoefficient group of every frequency band.

Next, among the subbands obtained at step S201, the subbands of HL, LH,HL indicating high-frequency coefficients are subjected to analysis atstep S202 for flattening (uniformalizing) the histogram of coefficientvalues. A coefficient conversion table is created at step S203 basedupon the result of analysis. According to this embodiment, the sixsubbands HH1, HL1, LH1, HH2, HL2 and LH2 are subjected to a coefficientconversion. However, it will suffice to apply conversion processing tothe coefficients of all or some of the subbands (frequency bands) inaccordance with the particular objective, as by applying the coefficientconversion only to HH1, HL1 and LH1. For example, if it is desired toimprove the contrast of high-frequency components, the coefficients ofHH1, LH2 and LH1 are selected. If it is desired to improve the contrastof the next lower range of frequency bands, then the coefficients ofHH2, HL2 and LH2 are selected.

By way of example, in a case where a histogram of coefficient values isuniformalized, it will suffice to create a cumulative histogram ofcoefficient values on a per-subband basis and normalize the shape of thecumulative histogram to obtain a coefficient conversion table(coefficient conversion function). Specifically, it will suffice tocreate a cumulative histogram (cumulative frequency distribution) byplotting coefficient values along a horizontal axis and the cumulativefrequency of the coefficient values along a vertical axis, and obtain acoefficient conversion table by substituting input coefficient valuesfor the horizontal axis of the cumulative histogram and outputcoefficient values for the vertical axis. An example of a coefficientconversion table thus created is a coefficient conversion table 301illustrated in FIG. 3. Specifically, FIG. 3 is a diagram illustrating anexample of a coefficient conversion table for converting coefficientvalues, on a per-frequency-band basis, calculated by thefrequency-component decomposition circuit 113. The horizontal axis is aplot of input coefficients and the vertical axis a plot of outputcoefficients. The curve 301 in FIG. 3 corresponds to the coefficientconversion table. The coefficient conversion table 301 is used in thecoefficient conversion circuit 114. It should be noted that actualcoefficient values take on a positive and negative values. Though theconversion function (conversion table) is originally an odd function,therefore, in FIG. 3 only the first quadrant thereof is illustrated.Further, a cumulative frequency distribution need not be used as is.Rather, the distribution may be smoothened and the coefficientconversion table may be created based upon the smoothened distribution.In such case the smoothing can be implemented as by a method ofapproximating the cumulative frequency distribution using a polynomial.

The scale along the vertical axis of the conversion table of FIG. 3 canbe enlarged or reduced at step S204 to such an extent that coefficientvalues after conversion will not exceed a maximum value. If this isdone, the absolute value of a coefficient value can be adjusted whilethe flatness of the histogram of coefficient values is maintained. Forexample, if the scale along the vertical axis is reduced, the values ofcoefficients after conversion become larger and the frequency ofappearance of each coefficient declines. Conversely, if the scale alongthe vertical axis is enlarged, the values of coefficients afterconversion become smaller and the frequency of appearance of eachcoefficient increases. This makes it possible to adjust the frequencyprocessing effect. Generally, a sharpening effect is obtained ifcoefficients are increased in value and a smoothing effect is obtainedif coefficients are reduced in value.

Next, at step S205, the coefficient conversion circuit 114 uses thecoefficient conversion table 301, which has been generated as set forthabove, to convert the coefficients generated at step S201. When acoefficient value of a subband is converted using the coefficientconversion table 301, the histogram of coefficient values after theconversion ideally takes on the form shown at 401 in FIG. 4.Specifically, FIG. 4 is a diagram illustrating an example of a frequencydistribution of coefficient values after conversion in a case where thecoefficient conversion has been performed using the coefficientconversion table 301. In FIG. 4, the horizontal axis is a plot ofcoefficient values and the vertical axis a plot of frequency ofappearance of the coefficient values. If the scale along the verticalaxis is altered, the height (frequency) and width (range of coefficientvalues) of the line 401 changes. It should be noted that a change in thecoefficient conversion characteristic (conversion table), such as achange in scale, can be performed by the changing circuit 116 based upona command, etc., from the control panel 110.

Thus, coefficient conversion processing according to this embodiment issuch that a coefficient conversion is carried out in such a manner thata frequency distribution of coefficient values after conversion willtake on a prescribed shape (e.g., a flat shape). In particular, it hasbeen confirmed that if the frequency distribution of coefficient valuesis uniformalized as shown in FIG. 4 and restoration processing(described later) is executed, an image of enhanced contrast isobtained. In other words, in accordance with such flattening of acoefficient-value histogram, frequency processing of an image can beexecuted effectively and efficiently.

An improvement in contrast according to the prior art and an improvementin contrast according to the above embodiment will be described. Animprovement in contrast by uniformalization of a density histogramaccording to the prior art improves the contrast of the overall image;it does not improve the contrast of a specific frequency band. Bycomparison, the above embodiment is capable of improving the contrast ofa specific frequency band. The contrast of a specific frequency bandsignifies the contrast of an image that has undergone reconstructionprocessing using only the coefficient of the specific frequency band.

The restoration circuit 115 executes processing for an inverse discretewavelet transform at step S206 using all of the coefficients after theyhave been changed by the coefficient conversion circuit 114 in the stepsS202 to S205.

FIG. 6C is a diagram illustrating the structure of the restorationcircuit 115. Entered coefficients are processed by two filters u and pand the signals are superimposed after upsampling to generate an outputimage signal x′. The processing involved is represented by the followingequations:x′ _(2n) =s′ _(n)−floor[(d′ _(n−1) +d′ _(n))/4]x′ _(2n+1) =d′ _(n)+floor[(x′ _(2n) +x′ _(2n+2))/2]

By virtue of the above processing, a one-dimensional inverse discretewavelet transform is applied to coefficients. A two-dimensional inversediscrete wavelet transform is achieved by executing the above-describedone-dimensional inverse transform successively along the horizontal andvertical directions of the image. As the details of this processing arewell known, they need not be described here.

In the foregoing, control is exercised so as to flatten the frequencydistribution of converted coefficient values of each of the frequencybands, as illustrated in FIG. 4. However, this does not impose alimitation upon the shape of the frequency distribution of convertedcoefficient values. For example, a shape in which the central portion ofthe distribution is peak-shaped, a linear shape that is ascending ordescending, or a shape that is based upon an ideal coefficient-valuefrequency distribution of an actual image can be adopted appropriatelyas the shape of the target frequency distribution. In a case where thefrequency distribution is made an ascending straight line, thissignifies that a normal frequency of appearance having a large amplitudein the reconstructed image increases in proportion to the wave height.It should be noted that such a frequency-distribution shape can bedecided experimentally in accordance with the object of processing orthe particular objective.

Furthermore, in a modification of frequency-distribution control, only acoordinate-value histogram within a prescribed local range ofcoefficient values can be flattened selectively using a conversion tableof the kind shown in FIG. 5. Specifically, FIG. 5 is a diagramillustrating an example of a coefficient conversion table in a mannersimilar to that of FIG. 3. Reference numeral 501 in FIG. 5 denotes arange of coefficient values in which the histogram is to be flattened,and reference numeral 502 represents the coefficient conversion table.The portion corresponding to the range 501 of coefficient values definesa shape in which the histogram is flattened. The portion correspondingto the range of other coefficient values defines a straight line whoseslope is 1, so that no conversion of coefficients will take place.Generally, in a high-frequency subband, the coefficient-value range 501(a range of small coefficient values) indicates a range corresponding tovalid information, such as microstructures in a continuous structure (ananatomical structure in the human body), and the range of largercoefficient values indicates a range corresponding to an edge portionsuch as the contour of a structure. In other words, the coefficientconversion for histogram uniformalization is made to act solely upon thecoefficient-value range 501, and the coefficient values in other rangesare not changed. It will suffice if the coefficient-value range 501 isset so as to take on a certain ratio with respect to the overall rangeof coefficient values (e.g., a ratio of 10% of the overall range).

In a case where the coefficient conversion table shown in FIG. 5 isused, contrast is enhanced in excellent fashion in the image area of thereconstructed image that corresponds to the coefficient-value range 501,and coefficients are held unchanged at an edge portion, which is animage area that corresponds to the other range of coefficient values. Aneffect obtained, therefore, is that the edge structure is preserved(i.e., false contouring does not occur). That is, effective andefficient frequency processing can be executed while suppressingartifacts such as so-called overshoot. Further, since large coefficientvalues are held unchanged, a fluctuation in the dynamic range of thereconstructed image is suppressed. Furthermore, in a case where such acoefficient conversion table is used, the contrast enhancing effect ofthe image area corresponding to the coefficient-value range 501 can beadjusted by altering the scale along the vertical axis, as mentionedabove. A further advantage is that the coefficient-value range 501 alsois changed in accordance with the image of interest, the subband ofinterest and the objective of processing.

Thus, in accordance with this embodiment, as described above, acoefficient conversion is performed in such a manner that a frequencydistribution of coefficient values after conversion defines a prescribedshape, whereby a desired contrast enhancing effect can be obtainedefficiently. For example, in a case where a coefficient conversion isperformed using a coefficient conversion table that is in accordancewith the histogram flattening method and restoration processing is thenexecuted, an excellent contrast enhancing effect can generally beobtained.

Furthermore, by using a coefficient conversion table so designed that aconversion characteristic based upon the histogram flattening method isadopted with regard to a range of coefficient values for which theabsolute value is equal to or less than a predetermined value while thecoefficient values are kept unchanged with regard to a range ofcoefficient values for which the absolute value exceeds thepredetermined value, valid portions that correspond to microstructuresor the like can be enhanced in contrast effectively and efficientlywithout producing artifacts such as false contours at edge portions inan image after processing. Furthermore, holding large coefficient valuesunchanged has the effect of enabling suppression of fluctuation in thedynamic range of the reconstructed image. The reason for this is thatthere are many cases where a fluctuation in dynamic range is produced bya change in large coefficient values. In addition, by altering the scalealong the vertical axis of the coefficient conversion table, coefficientvalues after conversion are altered at a prescribed ratio uniformly andthe contrast enhancing effect can be adjusted.

Furthermore, the decomposition of an image into frequency components isnot limited to a discrete wavelet transform. The decomposition can beachieved using other methods, such as by a Laplacian pyramid transform.Moreover, it is permissible to use a method of calculating high- andlow-frequency components employing the so-called running-mean method. Insuch case a high-frequency image is converted using a histogramuniformalizing method [processing equivalent to histogram equalizationin image space; see the above-mentioned reference (“Digital ImageProcessing for Understanding Images”)]. The high-frequency componentsare created by the frequency-component decomposition circuit 113, thehigh-frequency components are converted using a conversion table thathas been created by the coefficient conversion circuit 114 based uponthe histogram uniformalizing method, and the high-frequency componentsthat have been converted by the coefficient conversion circuit 114 areadded to the low-frequency components by the restoration circuit 115,whereby a processed image is obtained. Thus, though the presentembodiment has been described using a discrete wavelet transform, thetechnical idea of the embodiment is applicable also to a case where useis made of other processing for decomposing an image into frequencycomponents.

It goes without saying that the object of the invention is attained alsoby supplying a storage medium storing the program codes of the softwarefor performing the functions of the foregoing embodiment to a system oran apparatus, reading the program codes with a computer (e.g., a CPU orMPU) of the system or apparatus from the storage medium, and thenexecuting the program codes.

In this case, the program codes read from the storage medium implementthe novel functions of the embodiment and the storage medium storing theprogram codes constitutes the invention.

Examples of storage media that can be used for supplying the programcode are a floppy disk, hard disk, optical disk, magneto-optical disk,CD-ROM, CD-R, magnetic tape, non-volatile type memory card or ROM, etc.

Further, besides the case where the aforesaid functions according to theembodiment are implemented by executing the program codes read by acomputer, it goes without saying that the present invention covers acase where an operating system or the like running on the computerperforms a part of or the entire process in accordance with thedesignation of program codes and implements the functions according tothe embodiment.

It goes without saying that the present invention further covers a casewhere, after the program codes read from the storage medium are writtenin a function expansion board inserted into the computer or in a memoryprovided in a function expansion unit connected to the computer, a CPUor the like contained in the function expansion board or functionexpansion unit performs a part of or the entire process in accordancewith the designation of program codes and implements the functions ofthe above embodiment.

As many apparently widely different embodiments of the present inventioncan be made without departing from the spirit and scope thereof, it isto be understood that the invention is not limited to the specificembodiments thereof except as defined in the appended claims.

1. An image processing apparatus comprising: decomposing means fordecomposing image data into component values of a plurality of frequencybands; converting means for converting the component values in at leastone of the plurality of frequency bands; and generating means forgenerating an image based on the component values converted by saidconverting means, wherein said converting means converts the componentvalues so that frequency of appearance of the component values comesclose to a predetermined constant value within a range where thecomponent values are lower than a predetermined value.
 2. The apparatusaccording to claim 1, wherein said converting means converts thecomponent values so that distribution of frequency of appearance ofcomponent values is substantially flat.
 3. The apparatus according toclaim 1, wherein said converting means converts the component values byfixed ratio in a range where absolute values of the component values arelarger than a predetermined value.
 4. The apparatus according to claim2, wherein said converting means converts the component values based onaccumulated frequency distribution.
 5. The apparatus according to claim1, further comprising means for changing the predetermined value.
 6. Theapparatus according to claim 3, wherein the range where absolute valuesof the component values are larger than a predetermined valuecorresponds to an edge portion of the image data.
 7. The apparatusaccording to claim 1, wherein said decomposing means decomposes theimage data into component values of the plurality of frequency bandsusing a wavelet transform.
 8. The apparatus according to claim 1,wherein said decomposing means decomposes the image data into componentvalues of the plurality of frequency bands using a Laplacian transform.9. The apparatus according to claim 1, wherein said decomposing meansdecomposes the image data into component values of the plurality offrequency bands using a running-mean method.
 10. The apparatus accordingto claim 3 wherein the fixed ratio equals to one.
 11. An imageprocessing method comprising: a decomposing step of decomposing imagedata into component values of a plurality of frequency bands; aconverting step of converting the component values in at least one ofthe plurality of frequency bands; and a generating step of generating animage based on the component values converted by said converting means,wherein said converting step converts the component values so thatfrequency of appearance of the component values approaches to apredetermined constant value within a range where the component valuesare lower than a predetermined value.
 12. A computer program stored in acomputer-readable medium for causing a computer to execute the imageprocessing method set forth in claim
 11. 13. A computer-readable mediumstoring a computer program for causing a computer to execute the imageprocessing method set forth in claim 11.